Publications

@article{Dzialas2025,

title = {Dopamine Transporter Imaging as Objective Monitoring Biomarker in Parkinson's Disease},

author = {Verena Dzialas and Gérard N. Bischof and Kathrin Möllenhoff and Alexander Drzezga and Thilo van Eimeren},

doi = {10.1002/ana.27223},

issn = {1531-8249},

year  = {2025},

date = {2025-03-27},

urldate = {2025-03-27},

journal = {Annals of Neurology},

publisher = {Wiley},

abstract = {<jats:sec><jats:title>Objective</jats:title><jats:p>Although dopamine transporter (DaT) imaging is a valuable diagnostic biomarker, few studies have investigated its utility in objectively monitoring disease progression in patients with Parkinson's disease (PD). To date, no study has established a longitudinal relationship between the DaT signal decline and the motor symptom increase, potentially due to neglected factors such as brain regions, disease laterality, and symptom subtypes, which this study addresses.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>This cohort study included participants who met the Movement Disorder Society (MDS) criteria for PD, with longitudinal imaging and clinical data from the Parkinson's Progression Markers Initiative Database. Linear mixed model analyses were used to investigate the relationship between the DaT signal decline and the motor symptom severity increase over time. We hypothesized that a decline in putaminal DaT availability in the less affected hemisphere would be associated with increasing contralateral motor symptoms, measured by the Unified Parkinson's Disease Rating Scale (UPDRS). Additional models explored the effects of different brain regions (caudate and putamen), symptom categories (MDS UPDRSIII score with and without tremor items), and disease onset laterality (left or right hemisphere).</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We included 719 participants (443 male patients and 276 female patients; mean age = 62.2 ± 9.5 years) with 1,981 available data points. As hypothesized, we observed a significant association between the decrease in the less affected putaminal DaT signal and motor symptom increase in the contralateral body side, independent of including or excluding tremor scores.</jats:p></jats:sec><jats:sec><jats:title>Interpretation</jats:title><jats:p>Our findings support the use of repetitive DaT imaging for objectively monitoring PD progression. This could facilitate personalized disease tracking, subtyping, and intervention testing in the future. ANN NEUROL 2025</jats:p></jats:sec>},

keywords = {Biomarker, DaT imaging, Parkinson, SPECT},

pubstate = {published},

tppubtype = {article}

}

@article{Dzialas2024,

title = {Houston, We Have AI Problem! Quality Issues with Neuroimaging‐Based Artificial Intelligence in Parkinson's Disease: A Systematic Review},

author = {Verena Dzialas and Elena Doering and Helena Eich and Antonio P. Strafella and David E. Vaillancourt and Kristina Simonyan and Thilo van Eimeren},

doi = {10.1002/mds.30002},

issn = {1531-8257},

year  = {2024},

date = {2024-12-00},

urldate = {2024-12-00},

journal = {Movement Disorders},

volume = {39},

number = {12},

pages = {2130--2143},

publisher = {Wiley},

abstract = {In recent years, many neuroimaging studies have applied artificial intelligence (AI) to facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and intervention. The aim of this systematic review was to provide an overview of neuroimaging‐based AI studies and to assess their methodological quality. A PubMed search yielded 810 studies, of which 244 that investigated the utility of neuroimaging‐based AI for PD diagnosis, prognosis, or intervention were included. We systematically categorized studies by outcomes and rated them with respect to five minimal quality criteria (MQC) pertaining to data splitting, data leakage, model complexity, performance reporting, and indication of biological plausibility. We found that the majority of studies aimed to distinguish PD patients from healthy controls (54%) or atypical parkinsonian syndromes (25%), whereas prognostic or interventional studies were sparse. Only 20% of evaluated studies passed all five MQC, with data leakage, non‐minimal model complexity, and reporting of biological plausibility as the primary factors for quality loss. Data leakage was associated with a significant inflation of accuracies. Very few studies employed external test sets (8%), where accuracy was significantly lower, and 19% of studies did not account for data imbalance. Adherence to MQC was low across all observed years and journal impact factors. This review outlines that AI has been applied to a wide variety of research questions pertaining to PD; however, the number of studies failing to pass the MQC is alarming. Therefore, we provide recommendations to enhance the interpretability, generalizability, and clinical utility of future AI applications using neuroimaging in PD. },

keywords = {Artificial Intelligence, DaT imaging, Guidelines, Parkinson, Structural MRI},

pubstate = {published},

tppubtype = {article}

}

@article{Dzialas2024b,

title = {Structural underpinnings and long-term effects of resilience in Parkinson’s disease},

author = {Verena Dzialas and Merle C. Hoenig and Stéphane Prange and Gérard N. Bischof and and Alexander Drzezga and Thilo van Eimeren},

doi = {10.1038/s41531-024-00699-x},

issn = {2373-8057},

year  = {2024},

date = {2024-12-00},

urldate = {2024-12-00},

journal = {npj Parkinsons Dis.},

volume = {10},

number = {1},

publisher = {Springer Science and Business Media LLC},

abstract = {<jats:title>Abstract</jats:title><jats:p>Resilience in neuroscience generally refers to an individual’s capacity to counteract the adverse effects of a neuropathological condition. While resilience mechanisms in Alzheimer’s disease are well-investigated, knowledge regarding its quantification, neurobiological underpinnings, network adaptations, and long-term effects in Parkinson’s disease is limited. Our study involved 151 Parkinson’s patients from the Parkinson’s Progression Marker Initiative Database with available Magnetic Resonance Imaging, Dopamine Transporter Single-Photon Emission Computed Tomography scans, and clinical information. We used an improved prediction model linking neuropathology to symptom severity to estimate individual resilience levels. Higher resilience levels were associated with a more active lifestyle, increased grey matter volume in motor-associated regions, a distinct structural connectivity network and maintenance of relative motor functioning for up to a decade. Overall, the results indicate that relative maintenance of motor function in Parkinson’s patients may be associated with greater neuronal substrate, allowing higher tolerance against neurodegenerative processes through dynamic network restructuring.</jats:p>},

keywords = {DaT imaging, Parkinson, Resilience},

pubstate = {published},

tppubtype = {article}

}

@article{Banwinkler2024,

title = {Putaminal dopamine modulates movement motivation in Parkinson’s disease},

author = {Magdalena Banwinkler and Verena Dzialas and Lionel Rigoux and Adrian L Asendorf and Hendrik Theis and Kathrin Giehl and Marc Tittgemeyer and Merle C Hoenig and Thilo van Eimeren},

doi = {10.1093/brain/awae214},

issn = {1460-2156},

year  = {2024},

date = {2024-10-03},

urldate = {2024-10-03},

journal = {Brain},

volume = {147},

number = {10},

pages = {3352--3357},

publisher = {Oxford University Press (OUP)},

abstract = {<jats:title>Abstract</jats:title>

               <jats:p>The relative inability to produce effortful movements is the most specific motor sign of Parkinson’s disease, which is primarily characterized by loss of dopaminergic terminals in the putamen. The motor motivation hypothesis suggests that this motor deficit may not reflect a deficiency in motor control per se, but a deficiency in cost-benefit considerations for motor effort. For the first time, we investigated the quantitative effect of dopamine depletion on the motivation of motor effort in Parkinson’s disease.</jats:p>

               <jats:p>A total of 21 early-stage, unmedicated patients with Parkinson’s disease and 26 healthy controls were included. An incentivized force task was used to capture the amount of effort participants were willing to invest for different monetary incentive levels and dopamine transporter depletion in the bilateral putamen was assessed.</jats:p>

               <jats:p>Our results demonstrate that patients with Parkinson’s disease applied significantly less grip force than healthy controls, especially for low incentive levels. Congruously, decrease of motor effort with greater loss of putaminal dopaminergic terminals was most pronounced for low incentive levels. This signifies that putaminal dopamine is most critical to motor effort when the trade-off with the benefit is poor.</jats:p>

               <jats:p>Taken together, we provide direct evidence that the reduction of effortful movements in Parkinson’s disease depends on motivation and that this effect is associated with putaminal dopaminergic degeneration.</jats:p>},

keywords = {DaT imaging, Motivation, Parkinson},

pubstate = {published},

tppubtype = {article}

}

@article{Asendorf2024,

title = {Dynamic properties in functional connectivity changes and striatal dopamine deficiency in Parkinson's disease},

author = {Adrian L. Asendorf and Hendrik Theis and Marc Tittgemeyer and Lars Timmermann and Gereon R. Fink and Alexander Drzezga and Carsten Eggers and Marina C. Ruppert‐Junck and David J. Pedrosa and Merle C. Hoenig and Thilo van Eimeren},

doi = {10.1002/hbm.26776},

issn = {1097-0193},

year  = {2024},

date = {2024-07-15},

urldate = {2024-07-15},

journal = {Human Brain Mapping},

volume = {45},

number = {10},

publisher = {Wiley},

abstract = {<jats:title>Abstract</jats:title><jats:sec><jats:label/><jats:p>Recent studies in Parkinson's disease (PD) patients reported disruptions in dynamic functional connectivity (dFC, i.e., a characterization of spontaneous fluctuations in functional connectivity over time). Here, we assessed whether the integrity of striatal dopamine terminals directly modulates dFC metrics in two separate PD cohorts, indexing dopamine‐related changes in large‐scale brain network dynamics and its implications in clinical features. We pooled data from two disease‐control cohorts reflecting early PD. From the Parkinson's Progression Marker Initiative (PPMI) cohort, resting‐state functional magnetic resonance imaging (rsfMRI) and dopamine transporter (DaT) single‐photon emission computed tomography (SPECT) were available for 63 PD patients and 16 age‐ and sex‐matched healthy controls. From the clinical research group 219 (KFO) cohort, rsfMRI imaging was available for 52 PD patients and 17 age‐ and sex‐matched healthy controls. A subset of 41 PD patients and 13 healthy control subjects additionally underwent <jats:sup>18</jats:sup>F‐DOPA‐positron emission tomography (PET) imaging. The striatal synthesis capacity of <jats:sup>18</jats:sup>F‐DOPA PET and dopamine terminal quantity of DaT SPECT images were extracted for the putamen and the caudate. After rsfMRI pre‐processing, an independent component analysis was performed on both cohorts simultaneously. Based on the derived components, an individual sliding window approach (44 s window) and a subsequent k‐means clustering were conducted separately for each cohort to derive dFC states (reemerging intra‐ and interindividual connectivity patterns). From these states, we derived temporal metrics, such as average dwell time per state, state attendance, and number of transitions and compared them between groups and cohorts. Further, we correlated these with the respective measures for local dopaminergic impairment and clinical severity. The cohorts did not differ regarding age and sex. Between cohorts, PD groups differed regarding disease duration, education, cognitive scores and L‐dopa equivalent daily dose. In both cohorts, the dFC analysis resulted in three distinct states, varying in connectivity patterns and strength. In the PPMI cohort, PD patients showed a lower state attendance for the globally integrated (GI) state and a lower number of transitions than controls. Significantly, worse motor scores (Unified Parkinson's Disease Rating Scale Part III) and dopaminergic impairment in the putamen and the caudate were associated with low average dwell time in the GI state and a low total number of transitions. These results were not observed in the KFO cohort: No group differences in dFC measures or associations between dFC variables and dopamine synthesis capacity were observed. Notably, worse motor performance was associated with a low number of bidirectional transitions between the GI and the lesser connected (LC) state across the PD groups of both cohorts. Hence, in early PD, relative preservation of motor performance may be linked to a more dynamic engagement of an interconnected brain state. Specifically, those large‐scale network dynamics seem to relate to striatal dopamine availability. Notably, most of these results were obtained only for one cohort, suggesting that dFC is impacted by certain cohort features like educational level, or disease severity. As we could not pinpoint these features with the data at hand, we suspect that other, in our case untracked, demographical features drive connectivity dynamics in PD.</jats:p></jats:sec><jats:sec><jats:title>Practitioner Points</jats:title><jats:p><jats:list list-type="bullet">

<jats:list-item><jats:p>Exploring dopamine's role in brain network dynamics in two Parkinson's disease (PD) cohorts, we unraveled PD‐specific changes in dynamic functional connectivity.</jats:p></jats:list-item>

<jats:list-item><jats:p>Results in the Parkinson's Progression Marker Initiative (PPMI) and the KFO cohort suggest motor performance may be linked to a more dynamic engagement and disengagement of an interconnected brain state.</jats:p></jats:list-item>

<jats:list-item><jats:p>Results only in the PPMI cohort suggest striatal dopamine availability influences large‐scale network dynamics that are relevant in motor control.</jats:p></jats:list-item>

</jats:list></jats:p></jats:sec>},

keywords = {DaT imaging, Functional connectivity, Parkinson},

pubstate = {published},

tppubtype = {article}

}